Business Leaders and Economists Are Continually Involved in the Process of Trying to Forecast

8/6/2019 Business Leaders and Economists Are Continually Involved in the Process of Trying to Forecast

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Business forecasting methodsRob J Hyndman

November 8, 2009

Forecasting, planning and goals

Forecasting is a common statistical task in business, where it helps inform decisionsabout scheduling of production, transportation and personnel, and provides a guide tolong-term strategic planning. However, business forecasting is often done poorly and isfrequently confused with planning and goals. They are three different things.

Forecasting is about predicting the future as accurately as possible, given all theinformation available including historical data and knowledge of any future events thatmight impact the forecasts.

Goals are what you would like to happen. Goals should be linked to forecasts and plans,

but this does not always occur. Too often, goals are set without any plan for how toachieve them, and no forecasts for whether they are realistic.

Planning is a response to forecasts and goals. Planning involves determining theappropriate actions that are required to make your forecasts match your goals.

Forecasting should be an integral part of the decision-making activities of management,as it can play an important role in many areas of a company. Modern organizationsrequire short-, medium- and long-term forecasts, depending on the specific application.

Short-term forecasts are needed for scheduling of personnel, production and

transportation. As part of the scheduling process, forecasts of demand are often alsorequired.

Medium-term forecasts are needed to determine future resource requirements in order to purchase raw materials, hire personnel, or buy machinery and equipment.

Long-term forecasts are used in strategic planning. Such decisions must take account of market opportunities, environmental factors and internal resources.

An organization needs to develop a forecasting system involving several approaches to predicting uncertain events. Such forecasting systems require the development of expertise in identifying forecasting problems, applying a range of forecasting methods,selecting appropriate methods for each problem, and evaluating and refining forecastingmethods over time. It is also important to have strong organizational support for the useof formal forecasting methods if they are to be used successfully.


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Business leaders and economists are continually involved in the process of trying toforecast, or predict, the future of business in the economy. Business leaders engage in this

process because much of what happens in businesses today depends

on what is going to happen in the future. For example, if a business is trying to make a

decision about developing a revolutionary new automobile, it would be nice to knowwhether the economy is going to be in a recession or whether it will be booming whenthe automobile is released to the general public. If there is a recession, consumers will not

buy the automobile unless it can save them money, and the manufacturer will have spentmillions or billions of dollars on the development of a product that might not sell.

The process of attempting to forecast the future is not new. Most ancient civilizationsused some method for predicting the future. Today, computers with elaborate programsare often used to develop models to forecast future economic and business activity.Contemporary models of economic and business forecasting have been developed in thelast century. Today's forecasting models are considerably more statistical than they were

hundreds of years ago when the stars, and other mystical methods, were used to predictthe future. Almost every large business or government agency performs some type of formalized forecasting.

Forecasting in business is closely related to understanding the business cycle. Thefoundations of modern forecasting were laid in 1865 by William Stanley Jevons, whoargued that manufacturing had replaced agriculture as the dominant sector in Englishsociety. He studied the effects of economic fluctuations of the limiting factors of coal

production on economic development.

Forecasting has become big business around the world. Forecasters try to predict what the

stock markets will do, what the economy will do, what numbers to pick in the lottery,who will win sporting events, and almost anything one might name. Regardless of whodoes it, forecasting is done to identify what is likely to happen in the future so as to beable to benefit most from the events.

QUALITATIVE FORECASTING MODELS

Qualitative forecasting models have often proven to be most effective for short-term projections. In this method of forecasting, which works best when the scope is limited,experts in the appropriate fields are asked to agree on a common forecast. Two methodsare used frequently.

Delphi Method. This method involves asking various experts what they anticipate willhappen in the future relative to the subject under consideration. Experts in the automotiveindustry, for example, might be asked to forecast likely innovative enhancements for carsfive years from now. They are not expected to be precise, but rather to provide generalopinions.

Market Research Method. This method involves surveys and questionnaires about people's subjective reactions to changes. For example, a company might develop a newway to launder clothes; after people have had an opportunity to try the new method, theywould be asked for feedback about how to improve the processes or how it might be

made more appealing for the general public. This method is difficult because it is hard toidentify an appropriate sample that is representative of the larger audience for whom the product is intended.

QUANTITATIVE FORECASTING MODELS

Three quantitative methods are in common use.

Time-Series Methods. This forecasting model uses historical data to try to predict futureevents. For example, assume that you are interested in knowing how long a recession willlast. You might look at all past recessions and the events leading up to and surrounding

them and then, from that data, try to predict how long the current recession will last.


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A specific variable in the time series is identified by the series name and date. If grossdomestic product (GDP) is the variable, it might be identified as GDP2000.1 for the first-quarter statistics for the year 2000. This is just one example, and different groups mightuse different methods to identify variables in a time period.

Many government agencies prepare and release time-series data. The Federal Reserve,for example, collects data on monetary policy and financial institutions and publishes thatdata in the Federal Reserve Bulletin . These data become the foundation for makingdecisions about regulating the growth of the economy.

Time-series models provide accurate forecasts when the changes that occur in thevariable's environment are slow and consistent. When large-degree changes occur, theforecasts are not reliable for the long term. Since time-series forecasts are relatively easyand inexpensive to construct, they are used quite extensively.

The Indicator Approach. The U.S. government is a primary user of the indicator

approach of forecasting. The government uses such indicators as the Composite Index of Leading, Lagging, and Coincident Indicators, often referred to as Composite Indexes.The indexes predict by assuming that past trends and relationships will continue into thefuture. The government indexes are made by averaging the behavior of the differentindicator series that make up each composite series.

The timing and strength of each indicator series relationship with general businessactivity, reflected in the business cycle, change over time. This relationship makesforecasting changes in the business cycle difficult.

Econometric Models. Econometric models are causal models that statistically identify

the relationships between variables and how changes in one or more variables causechanges in another variable. Econometric models then use the identified relationship to predict the future. Econometric models are also called regression models.

There are two types of data used in regression analysis. Economic forecasting models predominantly use time-series data, where the values of the variables change over time.Additionally, cross-section data, which capture the relationship between variables at asingle point in time, are used. A lending institution, for example, might want to determinewhat influences the sale of homes. It might gather data on home prices, interest rates, andstatistics on the homes being sold, such as size and location. This is the cross-section datathat might be used with time-series data to try to determine such things as what size home

will sell best in which location.An econometric model is a way of determining the strength and statistical significance of a hypothesized relationship. These models are used extensively in economics to prove,disprove, or validate the existence of a casual relationship between two or more variables.It is obvious that this model is highly mathematical, using different statistical equations.

For the sake of simplicity, mathematical analysis is not addressed here. Just as there arethese qualitative and quantitative forecasting models, there are others equally assophisticated; however, the discussion here should provide a general sense of the natureof forecasting models.

THE FORECASTING PROCESS

When beginning the forecasting process, there are typical steps that must be followed.These steps follow an acceptable decision-making process that includes the followingelements:

1. Identification of the problem. Forecasters must identify what is going to beforecasted, or what is of primary concern. There must be a timeline attached to theforecasting period. This will help the forecasters to determine the methods to beused later.

2. Theoretical considerations. It is necessary to determine what forecasting has beendone in the past using the same variables and how relevant these data are to the


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problem that is currently under consideration. It must also be determined whateconomic theory has to say about the variables that might influence the forecast.

3. Data concerns. How easy will it be to collect the data needed to be able to makethe forecasts is a significant issue.

4. Determination of the assumption set. The forecaster must identify the assumptions

that will be made about the data and the process.5. Modeling methodology. After careful examination of the problem, the types of models most appropriate for the problem must be determined.

6. Preparation of the forecast. This is the analysis part of the process. After themodel to be used is determined, the analysis can begin and the forecast can be

prepared.7. Forecast verification. Once the forecasts have been made, the analyst must

determine whether they are reasonable and how they can be compared against theactual behavior of the data.

Each of the seven steps has substages; however, the steps that have been presented are the

major concerns to the forecaster. Those with a deep interest in forecasting might pursuemore in-depth treatments.

FORECASTING CONCERNS

Forecasting does present some problems. Even though very detailed and sophisticatedmathematical models might be used, they do not always predict correctly. There are somewho would argue that the future cannot be predicted at all period!

Some of the concerns about forecasting the future are that (1) predictions are made usinghistorical data, (2) they fail to account for unique events, and (3) they ignore coevolution

(developments created by our own actions). Additionally, there are psychologicalchallenges implicit in forecasting. An example of a psychological challenge is when

plans based on forecasts that use historical data become so confining as to prohibitmanagement freedom. It is also a concern that many decision makers feel that becausethey have the forecasting data in hand they have control over the future.

Regardless of the opponents to forecasting, the U.S. government, investment analysts, business managers, economists, and numerous others will continue to use forecastingtechniques to predict the future. It is imperative for the users of the forecasts tounderstand the information and use the results as they are intended.

BIBLIOGRAPHY

Fulmer, William, E. (2000). Shaping the Adaptive Organization: Landscapes, Learning,and Leading in Volatile Times . New York: AMACOM.

Moore, Geoffrey H. (1983). Business Cycles, Inflation, and Forecasting . Cambridge,MA: Ballinger.

Sherman, Howard, J., and Kolk, David X. (1996). Business Cycles and Forecasting . NewYork: HarperCollins.

Stock, James H., and Watson, Mark W., eds. (1993). Business Cycles, Indicators, and Forecasting . Chicago: University of Chicago Press.

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Business Leaders and Economists Are Continually Involved in the Process of Trying to Forecast